Creating Select Figures for Beltran-Pena et al 2020.¶

In [33]:
#pip install plotly==5.11.0
In [1]:
#pip install nbconvert
In [3]:
#pip install nbconvert[webpdf]
In [ ]:
In [2]:
import pandas as pd

import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline
import os
import csv
import xlrd
import seaborn as sns

import plotly.graph_objects as go
from ipywidgets import widgets
import plotly.express as px
from plotly.subplots import make_subplots

import scipy
import ipywidgets as widgets
In [ ]:
In [3]:
filepath = 'C:\\Users\\areid\\OneDrive\\ESPM Ph.D\\Research\\yield gap &pop proj Research\\Data\\MAIN Data sets\\'
In [4]:
Self_Sufficiency_Climate = pd.read_csv(filepath +'FINAL Self Sufficiency Ratios with Climate Change.csv')
In [5]:
Current_Self_Sufficiency = pd.read_csv(filepath +'FINAL Self-Sufficiency Ratios for year 2000 data (curent).csv')

Preparing Dataframes for figures

In [6]:
Self_Sufficiency_Climate = Self_Sufficiency_Climate.drop(columns=['Unnamed: 0'])
In [7]:
Self_Sufficiency_Climate.head()
Out[7]:
FAO Country Code UN Country Code ISO MagPie Region Code SSP Region Country SSP Scenario Population Scenario RCP Year Total kcal demand Sum of yield CC 4 major crops Sum of 18 crops w/o 4 major crops TOTAL Production with Climate Change (all 22 crops) Self-Sufficiency Ratio Reclassified Self-Sufficiency Ratios
0 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 2.6 2030 4.371143e+13 1.677106e+13 5.442117e+12 2.221318e+13 0.508178 0.508178
1 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 4.5 2030 4.371143e+13 1.698837e+13 5.442117e+12 2.243049e+13 0.513149 0.513149
2 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 6.0 2030 4.371143e+13 1.781240e+13 5.442117e+12 2.325451e+13 0.532001 0.532001
3 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 8.5 2030 4.371143e+13 1.725274e+13 5.442117e+12 2.269486e+13 0.519197 0.519197
4 16.0 50.0 BGD SAS ASIA Bangladesh SSP1-Baseline Low 2.6 2030 1.478124e+14 1.364770e+14 8.430541e+12 1.449075e+14 0.980347 0.980347
In [8]:
Current_Self_Sufficiency = Current_Self_Sufficiency.drop(columns = ['Unnamed: 0'])
In [9]:
Current_Self_Sufficiency = Current_Self_Sufficiency.rename(columns ={'Reclassified Self-Sufficiency Ratios (year 2000)':'Reclassified Self-Sufficiency Ratios'})
In [10]:
Current_Self_Sufficiency['Year'] = Current_Self_Sufficiency['Year'].astype(int)
In [11]:
Current_Self_Sufficiency.head()
Out[11]:
UN Country code FAO Country Code ISO MagPie Region Code SSP Region Country Year Historical 2000 Pop Total kcal production of 22 crops year 2000 Food supply (kcal/capita/day) Food supply for year 2000 (kcal/capita/YEAR) kcal consumption per country in 2000 (Food Supply * Population) Self-Sufficiency Ratios (year 2000) Reclassified Self-Sufficiency Ratios
0 231 238.0 ETH AFR MAF Ethiopia 2000 66224804.0 1.949408e+13 1786.0 651890.0 4.317129e+13 0.451552 0.451552
1 404 114.0 KEN AFR MAF Kenya 2000 31964557.0 1.382456e+13 2049.0 747885.0 2.390581e+13 0.578293 0.578293
2 450 129.0 MDG AFR MAF Madagascar 2000 15766806.0 6.431309e+12 2006.0 732190.0 1.154430e+13 0.557098 0.557098
3 454 130.0 MWI AFR MAF Malawi 2000 11148758.0 4.282963e+12 2204.0 804460.0 8.968730e+12 0.477544 0.477544
4 480 137.0 MUS AFR MAF Mauritius 2000 1185145.0 1.517476e+12 2904.0 1059960.0 1.256206e+12 1.207983 1.207983

Chloropleth Map for Self-Sufficiency Ratios in Year 2000 (Historical)

In [12]:
fig_1 = go.Figure(data=go.Choropleth(
    locations = Current_Self_Sufficiency['ISO'],
    z = Current_Self_Sufficiency["Reclassified Self-Sufficiency Ratios"],
    text = Current_Self_Sufficiency['Country'],
    colorbar=dict(
        title="Self-Sufficiency Ratio",
        titleside="top",
        tickmode="array",
        tickvals=[0, 0.5, 1, 1.5, 2.1],
        ticktext=["0","0.5", "1","1.5", ">2"],
        ticks="outside"
    ),
    colorscale ='PRGn',
    autocolorscale=False,
    reversescale=False,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_title = 'Self-Sufficiency Ratio',
))

fig_1.update_layout(
    title_text='Self-Sufficiency Ratios in Year 2000 (Historical)',
    geo=dict(
        showframe=True,
        showcoastlines=False,
        projection_type='equirectangular'
    )
)


fig_1.show()

Test Chloropleth Map for Self-Sufficiency Ratios in 2100 under RCP 8.5, SSP 3, Population Med

In [13]:
self_suff_rcp8_5_yr2100_ssp3_popMed = Self_Sufficiency_Climate.loc[(Self_Sufficiency_Climate['RCP']==8.5) & (Self_Sufficiency_Climate['Year']== 2100) & (Self_Sufficiency_Climate['SSP Scenario']== 'SSP3-Baseline')& (Self_Sufficiency_Climate['Population Scenario']== 'Med')]
In [14]:
fig_2 = go.Figure(data=go.Choropleth(
    locations = self_suff_rcp8_5_yr2100_ssp3_popMed['ISO'],
    z = self_suff_rcp8_5_yr2100_ssp3_popMed['Reclassified Self-Sufficiency Ratios'],
    text = self_suff_rcp8_5_yr2100_ssp3_popMed['Country'],
    colorscale = 'RdYlGn',
    autocolorscale=False,
    reversescale=False,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_title = 'Self-Sufficiency Ratio',
))

fig_2.update_layout(
    title_text='Self-Sufficiency Ratios in 2100 under RCP 8.5, SSP 3, Pop Med',
    geo=dict(
        showframe=True,
        showcoastlines=False,
        projection_type='equirectangular'
    )
)
#     annotations = [dict(
#         x=0.55,
#         y=0.1,
#         xref='paper',
#         yref='paper',
#         text='Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
#             CIA World Factbook</a>',
#         showarrow = False
   

fig_2.show()
In [15]:
#conda install -c plotly plotly-orca psutil requests
In [16]:
import os

if not os.path.exists("images"):
    os.mkdir("images")
In [17]:
#fig_2.write_image("images/fig1.jpeg")

Creating Widget¶

Widget for dataframes with data based on projection year

In [18]:
ALL ='ALL'
dropdown_year = widgets.Dropdown(options =    [2030, 2050, 2080, 2100])
output_year = widgets.Output()

def dropdown_year_eventhandler(change):
    output_year.clear_output()
    with output_year:
        display(Self_Sufficiency_Climate.loc[Self_Sufficiency_Climate['Year'] == change.new])
    
dropdown_year.observe(dropdown_year_eventhandler, names='value')   
display(dropdown_year) 
display(output_year)

Dropdown(options=(2030, 2050, 2080, 2100), value=2030)
Output()

Final widget which produces the various chloropleth maps under the different scenario combinations (Year, RCP, SSP, and United Nations Population Variant). In this widget you can choose to display the chloropleth map or explore the filtered dataset under the specified scenario combinations.

In [19]:
output = widgets.Output()
plot_output = widgets.Output()

dropdown_year = widgets.Dropdown(options =    [2000, 2030, 2050, 2080, 2100], description = 'Year:')
dropdown_RCP = widgets.Dropdown(options = [2.6, 4.5, 6.0, 8.5], description = 'RCP:')
dropdown_SSP = widgets.Dropdown(options = ['SSP1-Baseline', 'SSP2-Baseline', 'SSP3-Baseline'], description = 'SSP:')
dropdown_Population = widgets.Dropdown(options = ['Low', 'Med', 'High'], description = 'Population:')

def common_filtering(year, rcp, ssp, pop):
    output.clear_output()
    plot_output.clear_output()
    
    if (year == 2000):
        common_filter = Current_Self_Sufficiency
    else:
        common_filter = Self_Sufficiency_Climate.loc[(Self_Sufficiency_Climate['Year'] == year) & 
                                  (Self_Sufficiency_Climate['RCP'] == rcp) & (Self_Sufficiency_Climate['SSP Scenario'] == ssp) & (Self_Sufficiency_Climate['Population Scenario'] == pop)]
    
    with output:
        display(common_filter)
        
    with plot_output:
        if (year == 2000):
            fig = go.Figure(data=go.Choropleth(
                locations = common_filter['ISO'],
                z = common_filter['Reclassified Self-Sufficiency Ratios'],
                text = common_filter['Country'],
                colorscale = 'RdYlGn',
                autocolorscale=False,
                reversescale=False,
                marker_line_color='darkgray',
                marker_line_width=0.5,
                colorbar_title = 'Self-Sufficiency Ratio',
            ))

            fig.update_layout(
                title_text='Self-Sufficiency Ratios in year 2000', 
                geo=dict(
                    showframe=True,
                    showcoastlines=False,
                    projection_type='equirectangular'
                    )
                )


        
        else:
            fig = go.Figure(data=go.Choropleth(
                locations = common_filter['ISO'],
                z = common_filter['Reclassified Self-Sufficiency Ratios'],
                text = common_filter['Country'],
                colorscale = 'RdYlGn',
                autocolorscale=False,
                reversescale=False,
                marker_line_color='darkgray',
                marker_line_width=0.5,
                colorbar_title = 'Self-Sufficiency Ratio',
            ))

            fig.update_layout(
                title_text='Self-Sufficiency Ratios in '+ str(year) + ' under RCP ' + str(rcp) + ', '+ str(ssp) +', and ' + str(pop) + ' UN Population Variant', 
                geo=dict(
                    showframe=True,
                    showcoastlines=False,
                    projection_type='equirectangular'
                    )
                )
            
        fig.show()
        
def dropdown_year_eventhandler(change):
    common_filtering(change.new, dropdown_RCP.value, dropdown_SSP.value, dropdown_Population.value)
def dropdown_RCP_eventhandler(change):
    common_filtering(dropdown_year.value, change.new, dropdown_SSP.value, dropdown_Population.value)
def dropdown_SSP_eventhandler(change):
    common_filtering(dropdown_year.value, dropdown_RCP.value, change.new, dropdown_Population.value)
def dropdown_Population_eventhandler(change):
    common_filtering(dropdown_year.value, dropdown_RCP.value, dropdown_SSP.value, change.new)
    
dropdown_year.observe(dropdown_year_eventhandler, names='value')
dropdown_RCP.observe(dropdown_RCP_eventhandler, names='value')
dropdown_SSP.observe(dropdown_SSP_eventhandler, names='value')
dropdown_Population.observe(dropdown_Population_eventhandler, names = 'value')


# display(dropdown_year)
# display(dropdown_RCP)
# display(dropdown_SSP)
# display(dropdown_Population)



# display(output)
# display(plot_output)
In [ ]:
In [20]:
item_layout = widgets.Layout(margin='0 0 50px 0')
input_widgets = widgets.HBox([dropdown_year, dropdown_RCP, dropdown_SSP, dropdown_Population],layout=item_layout)
tab = widgets.Tab([output, plot_output], layout=item_layout)
tab.set_title(0, 'Dataset Exploration')
tab.set_title(1, 'Choropleth Map')

dashboard = widgets.VBox([input_widgets, tab])
display(dashboard)

VBox(children=(HBox(children=(Dropdown(description='Year:', options=(2000, 2030, 2050, 2080, 2100), value=2000…
In [ ]:

Box Plot¶

The code after this was preparing the dataframes to produce a box plot for a sensitivity analysis. However, I ended up creating a more complex figure in Tableau which was published in Beltran-Pena et al 2020.

In [21]:
Self_Sufficiency_Climate.head()
Out[21]:
FAO Country Code UN Country Code ISO MagPie Region Code SSP Region Country SSP Scenario Population Scenario RCP Year Total kcal demand Sum of yield CC 4 major crops Sum of 18 crops w/o 4 major crops TOTAL Production with Climate Change (all 22 crops) Self-Sufficiency Ratio Reclassified Self-Sufficiency Ratios
0 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 2.6 2030 4.371143e+13 1.677106e+13 5.442117e+12 2.221318e+13 0.508178 0.508178
1 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 4.5 2030 4.371143e+13 1.698837e+13 5.442117e+12 2.243049e+13 0.513149 0.513149
2 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 6.0 2030 4.371143e+13 1.781240e+13 5.442117e+12 2.325451e+13 0.532001 0.532001
3 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Low 8.5 2030 4.371143e+13 1.725274e+13 5.442117e+12 2.269486e+13 0.519197 0.519197
4 16.0 50.0 BGD SAS ASIA Bangladesh SSP1-Baseline Low 2.6 2030 1.478124e+14 1.364770e+14 8.430541e+12 1.449075e+14 0.980347 0.980347
In [22]:
Current_Self_Sufficiency.head()
Out[22]:
UN Country code FAO Country Code ISO MagPie Region Code SSP Region Country Year Historical 2000 Pop Total kcal production of 22 crops year 2000 Food supply (kcal/capita/day) Food supply for year 2000 (kcal/capita/YEAR) kcal consumption per country in 2000 (Food Supply * Population) Self-Sufficiency Ratios (year 2000) Reclassified Self-Sufficiency Ratios
0 231 238.0 ETH AFR MAF Ethiopia 2000 66224804.0 1.949408e+13 1786.0 651890.0 4.317129e+13 0.451552 0.451552
1 404 114.0 KEN AFR MAF Kenya 2000 31964557.0 1.382456e+13 2049.0 747885.0 2.390581e+13 0.578293 0.578293
2 450 129.0 MDG AFR MAF Madagascar 2000 15766806.0 6.431309e+12 2006.0 732190.0 1.154430e+13 0.557098 0.557098
3 454 130.0 MWI AFR MAF Malawi 2000 11148758.0 4.282963e+12 2204.0 804460.0 8.968730e+12 0.477544 0.477544
4 480 137.0 MUS AFR MAF Mauritius 2000 1185145.0 1.517476e+12 2904.0 1059960.0 1.256206e+12 1.207983 1.207983
In [23]:
Current_Self_Sufficiency = Current_Self_Sufficiency.rename(columns = {'UN Country code':'UN Country Code'})
In [24]:
Current_Self_Sufficiency = Current_Self_Sufficiency.rename(columns = {'Self-Sufficiency Ratios (year 2000)':'Self-Sufficiency Ratio'})
In [25]:
Current_future = Self_Sufficiency_Climate.append(Current_Self_Sufficiency, sort =False)

C:\Users\areid\AppData\Local\Temp\ipykernel_19280\1681316535.py:1: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
In [26]:
Current_future.columns
Out[26]:
Index(['FAO Country Code', 'UN Country Code', 'ISO', 'MagPie Region Code',
       'SSP Region', 'Country', 'SSP Scenario', 'Population Scenario', 'RCP',
       'Year', 'Total kcal demand', 'Sum of yield CC 4 major crops',
       'Sum of 18 crops w/o 4 major crops',
       'TOTAL Production with Climate Change (all 22 crops)',
       'Self-Sufficiency Ratio', 'Reclassified Self-Sufficiency Ratios',
       'Historical 2000 Pop', 'Total kcal production of 22 crops year 2000',
       'Food supply (kcal/capita/day)',
       'Food supply for year 2000 (kcal/capita/YEAR)',
       'kcal consumption per country in 2000 (Food Supply * Population)'],
      dtype='object')
In [27]:
Current_future = Current_future[['FAO Country Code', 'UN Country Code', 'ISO', 'MagPie Region Code',
       'SSP Region', 'Country', 'SSP Scenario', 'Population Scenario', 'RCP',
       'Year',  'Self-Sufficiency Ratio', 'Reclassified Self-Sufficiency Ratios']]
In [28]:
SSP1 =Current_future.loc[(Current_future['Population Scenario']=='Med') & (Current_future['SSP Scenario']=='SSP1-Baseline')]
SSP1.head()
Out[28]:
FAO Country Code UN Country Code ISO MagPie Region Code SSP Region Country SSP Scenario Population Scenario RCP Year Self-Sufficiency Ratio Reclassified Self-Sufficiency Ratios
1936 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Med 2.6 2030 0.496674 0.496674
1937 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Med 4.5 2030 0.501533 0.501533
1938 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Med 6.0 2030 0.519958 0.519958
1939 2.0 4.0 AFG SAS ASIA Afghanistan SSP1-Baseline Med 8.5 2030 0.507444 0.507444
1940 16.0 50.0 BGD SAS ASIA Bangladesh SSP1-Baseline Med 2.6 2030 0.955010 0.955010
In [29]:
table1 = pd.pivot_table(SSP1, values=['Self-Sufficiency Ratio'], index=['RCP', 'Year'],
                        aggfunc={'Self-Sufficiency Ratio': [min, max, np.mean]})
table1.reset_index()
Out[29]:
RCP Year Self-Sufficiency Ratio
max mean min
0 2.6 2030 7.096696 1.409869 0.000032
1 2.6 2050 8.667064 1.440098 0.000028
2 2.6 2080 11.069850 1.584658 0.000026
3 2.6 2100 12.900592 1.733397 0.000025
4 4.5 2030 7.166408 1.413409 0.000032
5 4.5 2050 8.609940 1.408488 0.000028
6 4.5 2080 11.038063 1.591206 0.000026
7 4.5 2100 12.505311 1.712793 0.000025
8 6.0 2030 7.180286 1.417084 0.000032
9 6.0 2050 8.792020 1.458045 0.000028
10 6.0 2080 11.155593 1.604396 0.000026
11 6.0 2100 13.061541 1.768654 0.000025
12 8.5 2030 7.233112 1.439925 0.000031
13 8.5 2050 8.677833 1.437371 0.000028
14 8.5 2080 11.002759 1.579919 0.000025
15 8.5 2100 12.620570 1.723244 0.000024
In [30]:
table1 = table1['Self-Sufficiency Ratio'].reset_index()
In [31]:
#Current_future.to_csv(filepath + 'Current+Future SSR combined for figures.csv')
In [32]:
pd.read_csv(filepath +'Global_Self_Sufficiency.csv')
Out[32]:
Unnamed: 0 Year RCP SSP Scenario Population Scenario Population Population Not Self-Sufficient (in millions)
0 0 2030 2.6 SSP1-Baseline High 8.635244e+09 -1804.776712
1 1 2030 2.6 SSP1-Baseline Low 8.269339e+09 -2170.682194
2 2 2030 2.6 SSP1-Baseline Medium 8.452291e+09 -1987.729398
3 3 2030 2.6 SSP2-Baseline High 8.635244e+09 -1317.046084
4 4 2030 2.6 SSP2-Baseline Low 8.269339e+09 -1682.951566
... ... ... ... ... ... ... ...
140 140 2100 8.5 SSP2-Baseline Medium 1.076235e+10 861.604304
141 141 2100 8.5 SSP3-Baseline High 1.544261e+10 9852.468930
142 142 2100 8.5 SSP3-Baseline Low 7.247303e+09 1657.164588
143 143 2100 8.5 SSP3-Baseline Medium 1.076472e+10 5174.580562
144 0 2000 NaN NaN NaN 5.939383e+09 -2047.097527

145 rows × 7 columns

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